"""simple docstring"""


import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Tuple:




						'''simple docstring'''



						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
												# expert layer
												lowerCamelCase__   =flax_key_tuple[:-1] + ("weight",)
												lowerCamelCase__   =torch.permute(__lowerCAmelCase  ,       (0, 2, 1)							)
						elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCAmelCase							):
												# linear layer
												lowerCamelCase__   =flax_key_tuple[:-1] + ("weight",)
												lowerCamelCase__   =flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
												lowerCamelCase__   =flax_key_tuple[:-1] + ("weight",)

						return flax_key_tuple, flax_tensor





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Optional[Any]:




						'''simple docstring'''



						if "metadata" in layer:
												lowerCamelCase__   =layer.split("metadata"							)
												lowerCamelCase__   ="".join(split_layer[0]							)[:-1]
												lowerCamelCase__   =[tuple(("metadata" + split_layer[1]).split("/"							)							)]
						elif "kvstore" in layer:
												lowerCamelCase__   =layer.split("kvstore"							)
												lowerCamelCase__   ="".join(split_layer[0]							)[:-1]
												lowerCamelCase__   =[tuple(("kvstore" + split_layer[1]).split("/"							)							)]

						else:
												lowerCamelCase__   =layer.split("/"							)
												lowerCamelCase__   ="/".join(split_layer[:-1]							)
												lowerCamelCase__   =(split_layer[-1],)

						if "kvstore/path" in layer:
												lowerCamelCase__   =F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
						elif "kvstore/driver" in layer:
												lowerCamelCase__   ="file"
						else:
												lowerCamelCase__   =checkpoint_info[layer]

						return curr_real_layer_name, split_layer, content





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Any:




						'''simple docstring'''



						lowerCamelCase__   =rename_keys(__lowerCAmelCase							)
						lowerCamelCase__   ={}
						for k, v in current_block.items():
												lowerCamelCase__   =v
						lowerCamelCase__   =new_current_block
						torch.save(__lowerCAmelCase  ,       __lowerCAmelCase							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = WEIGHTS_NAME							)				->   Any:




						'''simple docstring'''



						lowerCamelCase__   =convert_file_size_to_int(__lowerCAmelCase							)
						lowerCamelCase__   =[]
						lowerCamelCase__   ={}
						lowerCamelCase__   =0
						lowerCamelCase__   =0

						os.makedirs(__lowerCAmelCase  ,       exist_ok=__lowerCAmelCase							)
						with gfile.GFile(switch_checkpoint_path + "/checkpoint"  ,       "rb"							) as fp:
												lowerCamelCase__   =serialization.msgpack_restore(fp.read()							)["optimizer"]["target"]
												lowerCamelCase__   =flatten_dict(__lowerCAmelCase  ,       sep="/"							)

						lowerCamelCase__   ={}
						for layer in checkpoint_info.keys():
												lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =get_key_and_tensorstore_dict(
												    __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
												if curr_real_layer_name in all_layers:
																		lowerCamelCase__   =content
												else:
																		lowerCamelCase__   ={split_layer[-1]: content}

						for key in all_layers.keys():
												# open tensorstore file
												lowerCamelCase__   =ts.open(unflatten_dict(all_layers[key]							)							).result().read().result()
												lowerCamelCase__   =torch.tensor(__lowerCAmelCase							)
												lowerCamelCase__   =raw_weights.numel() * dtype_byte_size(raw_weights.dtype							)

												# use the renaming pattern from the small conversion scripts
												lowerCamelCase__   , lowerCamelCase__   =rename_base_flax_keys(tuple(key.split("/"							)							)  ,       __lowerCAmelCase							)
												lowerCamelCase__   ="/".join(__lowerCAmelCase							)

												# If this weight is going to tip up over the maximal size, we split.
												if current_block_size + weight_size > max_shard_size:
																		lowerCamelCase__   =os.path.join(
																		    __lowerCAmelCase  ,       weights_name.replace(".bin"  ,       F'''-{len(__lowerCAmelCase							)+1:05d}-of-???.bin'''							)							)
																		rename_and_save_block(__lowerCAmelCase  ,       __lowerCAmelCase							)
																		sharded_state_dicts.append(current_block.keys()							)
																		del current_block
																		lowerCamelCase__   ={}
																		lowerCamelCase__   =0

												lowerCamelCase__   =raw_weights.to(getattr(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
												current_block_size += weight_size
												total_size += weight_size

						# Add the last block
						lowerCamelCase__   =os.path.join(__lowerCAmelCase  ,       weights_name.replace(".bin"  ,       F'''-{len(__lowerCAmelCase							)+1:05d}-of-???.bin'''							)							)
						rename_and_save_block(__lowerCAmelCase  ,       __lowerCAmelCase							)
						sharded_state_dicts.append(current_block.keys()							)

						# If we only have one shard, we return it
						if len(__lowerCAmelCase							) == 1:
												return {weights_name: sharded_state_dicts[0]}, None

						# Otherwise, let's build the index
						lowerCamelCase__   ={}
						lowerCamelCase__   ={}
						for idx, shard in enumerate(__lowerCAmelCase							):
												lowerCamelCase__   =weights_name.replace(
												    ".bin"  ,       F'''-{idx+1:05d}-of-{len(__lowerCAmelCase							):05d}.bin'''							)  # len(sharded_state_dicts):05d}
												lowerCamelCase__   =os.path.join(__lowerCAmelCase  ,       weights_name.replace(".bin"  ,       F'''-{idx+1:05d}-of-???.bin'''							)							)
												os.rename(__lowerCAmelCase  ,       os.path.join(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
												lowerCamelCase__   =shard
												for key in shard:
																		lowerCamelCase__   =shard_file

    # Add the metadata
						lowerCamelCase__   ={"total_size": total_size}
						lowerCamelCase__   ={"metadata": metadata, "weight_map": weight_map}

						with open(os.path.join(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       "w"  ,       encoding="utf-8"							) as f:
												lowerCamelCase__   =json.dumps(__lowerCAmelCase  ,       indent=2  ,       sort_keys=__lowerCAmelCase							) + "\n"
												f.write(__lowerCAmelCase							)

						return metadata, index


if __name__ == "__main__":
	a				=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--switch_t5x_checkpoint_path',
	    default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
	    type=str,
	    required=False,
	    help='Path to a directory containing a folder per layer. Follows the original Google format.',
	)
	parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
	parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
	    type=str,
	    required=False,
	    help='Path to the output pytorch model.',
	)
	a				=parser.parse_args()
	shard_on_the_fly(
	    args.switch_tax_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.max_shard_size,
	    args.dtype,
	)





def      lowerCamelCase_					(   )				->   int:




						'''simple docstring'''



						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

						lowerCamelCase__   =SwitchTransformersConfig.from_pretrained("google/switch-base-8"							)
						config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"							)
						lowerCamelCase__   =SwitchTransformersForConditionalGeneration.from_pretrained(
						    "/home/arthur_huggingface_co/transformers/switch_converted"  ,       device_map="auto"							)

						lowerCamelCase__   =TaTokenizer.from_pretrained("t5-small"							)
						lowerCamelCase__   ="A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

						lowerCamelCase__   =tokenizer(__lowerCAmelCase  ,       return_tensors="pt"							).input_ids
						lowerCamelCase__   =model.generate(__lowerCAmelCase  ,       decoder_start_token_id=0							)
						print(tokenizer.decode(out[0]							)							)



"""simple docstring"""


a				=range(2, 20 + 1)
a				=[10**k for k in range(ks[-1] + 1)]
a				={}





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Any:




						'''simple docstring'''



						lowerCamelCase__   =sum(a_i[j] for j in range(__lowerCAmelCase  ,       len(__lowerCAmelCase							)							)							)
						lowerCamelCase__   =sum(a_i[j] * base[j] for j in range(min(len(__lowerCAmelCase							)  ,       __lowerCAmelCase							)							)							)

						lowerCamelCase__   , lowerCamelCase__   =0, 0
						lowerCamelCase__   =n - i

						lowerCamelCase__   =memo.get(__lowerCAmelCase							)

						if sub_memo is not None:
												lowerCamelCase__   =sub_memo.get(__lowerCAmelCase							)

												if jumps is not None and len(__lowerCAmelCase							) > 0:
																		# find and make the largest jump without going over
																		lowerCamelCase__   =-1
																		for _k in range(len(__lowerCAmelCase							) - 1  ,       -1  ,       -1							):
																								if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																														lowerCamelCase__   =_k
																														break

																		if max_jump >= 0:
																								lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =jumps[max_jump]
																								# since the difference between jumps is cached, add c
																								lowerCamelCase__   =diff + c
																								for j in range(min(__lowerCAmelCase  ,       len(__lowerCAmelCase							)							)							):
																														lowerCamelCase__   , lowerCamelCase__   =divmod(__lowerCAmelCase  ,       10							)
																								if new_c > 0:
																														add(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)

												else:
																		lowerCamelCase__   =[]
						else:
												lowerCamelCase__   ={c: []}
												lowerCamelCase__   =sub_memo

						if dn >= max_dn or c + diff >= base[k]:
												return diff, dn

						if k > ks[0]:
												while True:
																		# keep doing smaller jumps
																		lowerCamelCase__   , lowerCamelCase__   =next_term(__lowerCAmelCase  ,       k - 1  ,       i + dn  ,       __lowerCAmelCase							)
																		diff += _diff
																		dn += terms_jumped

																		if dn >= max_dn or c + diff >= base[k]:
																								break
						else:
												# would be too small a jump, just compute sequential terms instead
												lowerCamelCase__   , lowerCamelCase__   =compute(__lowerCAmelCase  ,       __lowerCAmelCase  ,       i + dn  ,       __lowerCAmelCase							)
												diff += _diff
												dn += terms_jumped

						lowerCamelCase__   =sub_memo[c]

						# keep jumps sorted by # of terms skipped
						lowerCamelCase__   =0
						while j < len(__lowerCAmelCase							):
												if jumps[j][1] > dn:
																		break
												j += 1

						# cache the jump for this value digitsum(b) and c
						sub_memo[c].insert(__lowerCAmelCase  ,       (diff, dn, k)							)
						return (diff, dn)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   List[str]:




						'''simple docstring'''



						if i >= n:
												return 0, i
						if k > len(__lowerCAmelCase							):
												a_i.extend([0 for _ in range(k - len(__lowerCAmelCase							)							)]							)

						# note: a_i -> b * 10^k + c
						# ds_b -> digitsum(b)
						# ds_c -> digitsum(c)
						lowerCamelCase__   =i
						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =0, 0, 0
						for j in range(len(__lowerCAmelCase							)							):
												if j >= k:
																		ds_b += a_i[j]
												else:
																		ds_c += a_i[j]

						while i < n:
												i += 1
												lowerCamelCase__   =ds_c + ds_b
												diff += addend
												lowerCamelCase__   =0
												for j in range(__lowerCAmelCase							):
																		lowerCamelCase__   =a_i[j] + addend
																		lowerCamelCase__   , lowerCamelCase__   =divmod(__lowerCAmelCase  ,       10							)

																		ds_c += a_i[j]

												if addend > 0:
																		break

						if addend > 0:
												add(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
						return diff, i - start_i





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						for j in range(__lowerCAmelCase  ,       len(__lowerCAmelCase							)							):
												lowerCamelCase__   =digits[j] + addend
												if s >= 10:
																		lowerCamelCase__   , lowerCamelCase__   =divmod(__lowerCAmelCase  ,       10							)
																		lowerCamelCase__   =addend // 10 + quotient
												else:
																		lowerCamelCase__   =s
																		lowerCamelCase__   =addend // 10

												if addend == 0:
																		break

						while addend > 0:
												lowerCamelCase__   , lowerCamelCase__   =divmod(__lowerCAmelCase  ,       10							)
												digits.append(__lowerCAmelCase							)





def      lowerCamelCase_					(   __lowerCAmelCase = 10**15							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =[1]
						lowerCamelCase__   =1
						lowerCamelCase__   =0
						while True:
												lowerCamelCase__   , lowerCamelCase__   =next_term(__lowerCAmelCase  ,       20  ,       i + dn  ,       __lowerCAmelCase							)
												dn += terms_jumped
												if dn == n - i:
																		break

						lowerCamelCase__   =0
						for j in range(len(__lowerCAmelCase							)							):
												a_n += digits[j] * 10**j
						return a_n


if __name__ == "__main__":
	print(F'''{solution() = }''')


